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High-Dimensional Covariance Matrix Estimation
Paperback
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- Book Synopsis
- This book presents covariance matrix estimation and related aspects of random matrix theory. It focuses on the sample covariance matrix estimator and provides a holistic description of its properties under two asymptotic regimes: the traditional one, and the high-dimensional regime that better fits the big data context. It draws attention to the deficiencies of standard statistical tools when used in the high-dimensional setting, and introduces the basic concepts and major results related to spectral statistics and random matrix theory under high-dimensional asymptotics in an understandable and reader-friendly way. The aim of this book is to inspire applied statisticians, econometricians, and machine learning practitioners who analyze high-dimensional data to apply the recent developments in their work.
- About The Author
- Aygul Zagidullina received her Ph.D. in Quantitative Economics and Finance from the University of Konstanz, Germany, with a specialization in the areas of financial econometrics and statistical modeling. Her research interests include estimation of high-dimensional covariance matrices, machine learning, factor models and neural networks.
- Product Details
-
- ISBN
- 9783030800642
- Format
- Paperback
- Publisher
- Springer, (30 October 2021)
- Number of Pages
- 115
- Weight
- 195 grams
- Language
- English
- Dimensions
- 235 x 155 x 7 mm
- Series:
- See all books in this series
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